Abstract
The increasing cost of health care has motivated the drive towards preventive medicine, where the primary concern is recognizing disease risk and taking action at the earliest stage. We present an application of deep learning to derive robust patient representations from the electronic health records and to predict future diseases. Experiments showed promising results in different clinical domains, with the best performances for liver cancer, diabetes, and heart failure.
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Notes
- 1.
In this architecture, each patient can be described by just one single vector (as done in this study) or by a bag of vectors computed in, e.g., predefined temporal windows.
- 2.
While this study focuses on future disease prediction, it should be noted that the patient representation derived from the stack of denoising autoencoders can also be applied to unsupervised tasks (e.g., patient clustering and similarity) as well as to other supervised applications (e.g., personalized prescriptions).
- 3.
While in this study we favored a basic pipeline to process EHRs, it should be noted that more sophisticated techniques might lead to better features as well as to better predictive results.
- 4.
All parameters in the feature learning models were identified through preliminary experiments, not reported here for brevity, on the validation set.
- 5.
This experiment only evaluates the prediction of new diseases for each patient, therefore not considering the re-diagnosis of a disease previously reported.
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Miotto, R., Li, L., Dudley, J.T. (2016). Deep Learning to Predict Patient Future Diseases from the Electronic Health Records. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_66
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DOI: https://doi.org/10.1007/978-3-319-30671-1_66
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